1865 results about "Semantic analytics" patented technology

The invention relates to a multi-data standard definition conflict resolution method based on a large model and a knowledge graph, and belongs to the field of public data governance and data standardization. The method comprises the following steps: extracting and preprocessing a multi-data standard definition; defining semantic representation and large model analysis: performing semantic analysis on the definition of each data item by using a large model, and generating a semantic embedding vector and a key feature; knowledge graph construction and entity alignment: constructing a knowledge graph containing all data items, and connecting data item nodes with similar semantics in different standards by adopting an entity alignment algorithm to form a candidate alignment relationship; conflict detection and type identification: aiming at the aligned data item definition, comparing attributes and values, identifying definition conflict points, and classifying and marking conflict types; and carrying out conflict resolution and unified definition generation by using a globally optimized conflict resolution algorithm. According to the method, the problems of data islands and semantic conflicts caused by inconsistent data standard definitions in the prior art are solved.

The invention provides a compliance auditing method and system for multi-modal content. The method comprises the following steps: performing feature extraction on unstructured to-be-audited multi-modal content to obtain a structured feature vector; performing image-text semantic association on the text semantic feature vector and the image visual feature vector to obtain a fusion feature vector involving image-text semantic contradiction; constructing a domain knowledge graph based on the compliance guidance data of the domain to which the to-be-audited multi-modal content belongs; inputting the fusion feature vector into a domain knowledge graph, and performing compliance rule retrieval by adopting a sub-graph matching algorithm to determine a violation type corresponding to the fusion feature vector and a violated compliance term; and generating an interactive compliance audit report. The system comprises functional modules for realizing the steps in a one-to-one correspondence manner. According to the technical scheme, the problem that cross-modal semantic analysis of an existing multi-modal content compliance auditing method is not accurate can be solved.

The invention discloses a method for analyzing a matching degree between a demand and an output result based on text semantics. The method comprises the following steps: step 1, labeling a data set; step 2, technical document preprocessing; 3, training and predicting a single-parameter model; 4, integrating prediction results of the multi-parameter model; the method has the beneficial effects thatthe method is simple; deep learning and the NLP technology are applied to the field of project association degree calculation of enterprise project management for the first time. Calculating an association matching degree between the two projects according to project requirements and result description; the associated project positioning difficulty is effectively reduced; meanwhile, the demand side can be helped to quickly and efficiently locate high-quality projects adapting to the demand of the demand side; time and resource investment for achievement screening and matching are greatly reduced, the association matching degree between projects is calculated by means of text data of existing project achievement technical documents and project declaration guidelines, and then large enterprises are assisted in screening high-quality projects with the high matching degree in the project bidding and tendering link.

The invention relates to the field of image processing, and discloses an intelligent drawing checking method and system based on a multi-modal large language model, and the method comprises the steps: obtaining a to-be-checked target engineering design drawing and a to-be-checked task description; generating a global overview drawing based on the target engineering design drawing; performing global semantic analysis according to the global overview map and the review task description through a multi-modal large language model, and generating a global semantic analysis result and to-be-reviewed local area proposal information; cutting a local image from the design drawing; performing element identification analysis on the local image through a multi-modal large language model to obtain local structured information; and performing information fusion processing on the local structured information and the global semantic analysis result, generating complete drawing information, performing compliance verification and defect positioning on the complete drawing information and the structured specification knowledge base, and generating a review report. According to the method, intelligent review of the power grid engineering design drawing can be realized, the review efficiency and accuracy are improved, and meanwhile, the resource consumption is reduced.

The invention provides a low-code dynamic configuration method based on a natural language, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out semantic analysis on a natural language description input by a user through an intelligent agent, extracting a page type, a field attribute, a verification rule and interaction logic, and generating structured configuration data; dynamically generating JSON metadata for describing page layout, field components, verification rules and interaction logic according to a preset template on the basis of the structured configuration data; according to page function requirements, interface metadata matched with the JSON metadata is generated, and the interface metadata comprises a request address, a parameter list and corresponding SQL (Structured Query Language) operation logic; and analyzing the JSON metadata by utilizing a low-code configuration rendering engine, dynamically generating an interactive management function page, and modifying the JSON metadata in real time and re-rendering the page through natural language interaction between a user and an intelligent agent.

The invention discloses an AI-based animation split script automatic generation and visual preview method and system, and the method comprises the following steps: 1, receiving a natural language script text inputted by a user, the natural language script text comprising scene description, role action, dialogue and shot indication information; step 2, performing semantic analysis and structured analysis on the script text based on a natural language processing technology, and identifying and extracting key narrative elements; by introducing an artificial intelligence technology, end-to-end automatic generation and interactive optimization from a character script to a dynamic split rehearsal video are realized, the system can deeply understand scenes, actions, role emotions and shot languages in the script, corresponding visual elements are automatically matched and generated, and the dynamic split rehearsal effect is improved. And the timeline and the rhythm conforming to the film and television grammar are constructed, so that the efficiency and the consistency of the split creation are greatly improved, and the professional threshold and the manufacturing cost are reduced.

The invention provides an RAG knowledge base construction method and system based on hierarchical semantic indexes. The method belongs to the cross technical field of artificial intelligence and information retrieval. The method comprises the following steps: performing multi-level semantic analysis on an input original document set to generate document semantic hierarchical structure data; constructing a hierarchical semantic index tree based on the document semantic hierarchical structure data; performing dynamic knowledge graph initialization according to the hierarchical semantic index tree to generate an initial dynamic cognitive graph; and collecting real-time interaction data through a user feedback interface and a new data access module, and performing incremental updating on the initial dynamic cognitive map to form a knowledge representation system supporting life cycle evolution. Through multi-level semantic analysis and construction of a hierarchical semantic index tree (HSIT), deep semantic analysis can be performed on an original document set, the context integrity of knowledge is ensured, structured storage is realized, the knowledge can be expressed and stored more accurately, and information loss or semantic ambiguity is avoided.

The invention discloses a multi-source heterogeneous data synchronization system, and relates to the field of computer information management application. The system fuses key technologies such as flow dynamic regulation and control, self-adaptive load balancing and intelligent semantic mapping. Through a real-time network state monitoring and self-adaptive flow regulation and control algorithm, the data transmission rate is dynamically adjusted according to the server resource utilization rate, the data priority and the network bandwidth, data backlog and system overload are avoided, and the robustness and the real-time performance of a synchronization task are remarkably improved. The intelligent field mapping engine is based on semantic analysis, word vector representation and a machine learning model, automatically recognizes field semantic consistency of heterogeneous data sources, generates an optimal mapping rule and dynamically optimizes a mapping result, the manual configuration cost is reduced, and the mapping accuracy rate reaches 98% or above. The system has high expandability and self-learning ability, is suitable for a large-scale multi-source heterogeneous data integration scene, and provides an efficient, intelligent and self-adaptive full-stack solution for a data synchronization task.

The invention discloses a retrieval generation method and system based on multi-agent collaboration, a terminal and a medium, and relates to the field of artificial intelligence. Performing semantic analysis on the input word embedding converted by the natural language query instruction through a query analysis agent, and determining a semantic intention vector; performing reinforcement learning and meta learning on the semantic intention vector through a strategy construction agent, and determining a retrieval strategy; performing semantic enhancement on the semantic intention vector according to knowledge graph node embedding to obtain a semantic enhancement vector; determining a data channel according to the semantic enhancement vector, a retrieval strategy and a real-time system load, and calling the data channel for retrieval to obtain candidate documents; and generating a target answer according to each candidate document based on an adaptive reflection feedback mechanism in combination with an auto-encoder and a generative adversarial network. The problems that the prior art depends on a fixed retrieval strategy, has limitation when facing complex query, multi-round interaction and cross-modal data fusion, is easily interfered by noise and is not accurate enough in semantic matching are effectively solved.

The invention relates to the technical field of semantic analysis, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses an intelligent correction method and device based on semantic analysis, equipment and a medium. The method comprises the following steps: extracting a structured document content from an explanation data source, extracting an auxiliary explanation text corresponding to each unit in the structured document content from the explanation data source, carrying out semantic understanding processing on the structured document content and the auxiliary explanation texts by utilizing a semantic analysis module to generate a semantic analysis result, and executing a correction operation through an intelligent correction module based on the semantic analysis result. And after the correction operation is completed, the structured document content and the auxiliary explanation text are input into a format reconstruction module, and a specification-combined document is generated and output. According to the method, semantic understanding is carried out by combining structured document content and the auxiliary explanation text, and an automatic and standardized document compliance processing flow is realized based on intelligent correction and format reconstruction driven by a compliance semantic analysis result.

The invention discloses a variation test method and device based on an intelligent automation script. The method comprises the following steps: extracting a statement execution sequence and a branch dependency relationship in an original source code by using a context-aware syntax tree, and generating a control flow diagram and a data flow diagram; performing code semantic analysis on the multi-dimensional code features by using a large language model, identifying potential defect types and variation rules, predicting the test efficiency of variants in combination with a deep Q learning model, and generating to-be-processed variants; performing grammar check and equivalence analysis on the to-be-processed variants to obtain effective variants, and testing the effective variants; variation test indexes are calculated, code weak points are positioned, and test blind areas are identified according to survival variants, so that a visual analysis report is generated; and iteratively generating a new test case according to the current survival variant and the visual analysis report, and testing the current survival variant by using the new test case. According to the invention, the variation test efficiency can be improved.

The invention provides a natural language text data intelligent classification method and system based on deep learning, and relates to the technical field of natural language processing, and the method comprises the steps: 1, employing a context awareness mechanism to analyze the real semantics of a target vocabulary according to an antagonistic variant existing in a text, and obtaining a target vocabulary; in combination with a word meaning library and a pre-training process of a dynamic learning rate adjustment strategy, generating a candidate replacement vocabulary set with consistent semantics; and step 2, based on the candidate replacement vocabulary set, performing multi-dimensional semantic similarity calculation and emotional tendency discrimination, determining applicable vocabularies conforming to an original culture background through a context adaptation strategy, and generating a standardized text sequence. According to the method, through multi-dimensional semantic analysis, cultural context fusion, cross-granularity feature construction and dynamic parameter correction, the accuracy and adaptability of natural language text classification are realized.

The invention discloses an MCP generation and calling method and device in a low-code platform, equipment and a medium, and relates to the field of artificial intelligence low-code, and the method comprises the steps: carrying out the semantic analysis and structure standardization of initial metadata generated in the application development process of the low-code platform, and obtaining target metadata; converting the target metadata into an MCP description file, converting a business description corresponding to the MCP service in the initial metadata into a natural language description which can be understood by a large model, and integrating the natural language description into the MCP description file to obtain a target MCP description file; constructing a standardized MCP warehouse, and managing the MCP service corresponding to the target MCP description file by using the MCP warehouse; and acquiring a user request by utilizing the large model, and performing MCP calling on the MCP service by utilizing the large model based on the current running state data in the application running process, so that the large model responds to the user request. According to the method, deep fusion of low codes and large models is promoted.

The invention discloses an information management method and system based on an electronic bidding transaction platform, and relates to the technical field of information management. By constructing a multi-dimensional and multi-level information management system, deep mining and intelligent identification of potential association relationships and behavior patterns among bidding units are realized, so that the capability of the electronic bidding platform in the aspects of risk prevention and control and abnormal behavior identification is effectively improved. By fusing multi-source heterogeneous data such as industry and commerce registration information, bidding history, project contracts and the like and introducing atlas modeling and semantic analysis technologies, key risk factors such as legal person penetration, historical cooperation, high bidding document similarity and the like can be accurately identified, then the risk association strength between bidding units is quantitatively evaluated, and the risk assessment efficiency is improved. And a visual map and an auxiliary auditing mechanism are formed, so that the judgment efficiency of supervisors is improved.

The invention specifically relates to the technical field of natural language processing, and discloses an intelligent interactive customer service system based on an AI large model, which comprises a multi-modal input analysis module, an intention preliminary detection module, a large model semantic understanding module, a dynamic knowledge base association module, a dialogue strategy generation module, a natural language generation module and a service feedback work order module, through deep cooperation of a multi-modal input analysis module and a large model semantic understanding module, entity and attribute information is accurately extracted, context semantic association information is mined, and through the cooperative effect of an intention preliminary detection module, a dynamic knowledge base association module and a dialogue strategy generation module, based on a semantic analysis result output by a large model, a dialogue strategy is generated. According to the method and the system, matching calculation is carried out with the constructed dynamic knowledge base, rich knowledge support is provided for solving complex problems, personalized knowledge content is recommended for different users through the natural language generation module and the service feedback work order module, and the reply matching degree and the complex problem processing capacity are greatly improved.

The invention provides an intelligent data labeling method and system based on multi-modal fusion and large model verification, belongs to the field of artificial intelligence and data processing, and innovatively fuses multi-modal information such as an OCR recognition result, a layout structure, original image visual features and deep semantic analysis of a large language model (LLM). And a precise automatic labeling result credibility evaluation mechanism is constructed. According to the method, various errors in automatic labeling can be accurately recognized and adaptively corrected, and the errors comprise conventional error correction based on hard coding rules and complex semantic error correction driven by LLM. Meanwhile, the system can continuously optimize the data labeling capability of the system through an efficient man-machine cooperation and closed-loop feedback learning mechanism, and automatically precipitate domain knowledge assets. The invention aims to solve the problems of recognition accuracy bottleneck, heavy manual proofreading burden, lack of intelligent judgment and error correction, knowledge accumulation lag and the like in traditional document data labeling, so that the efficiency, accuracy and automation level of document data labeling are remarkably improved.

The invention discloses a person and post accurate matching system based on an artificial intelligence technology, and relates to the technical field of human resource recruitment applied to the artificial intelligence technology, and the system comprises a first-layer feature vector matching module which is used for inputting text information of job seekers and enterprise posts, analyzing the text information through a natural language processing NLP technology, and obtaining a second-layer feature vector matching module; identifying and extracting structured features, outputting a structured feature set, and mapping the structured feature set into feature vectors by adopting a pre-training model; according to the method, through a triple matching mode of combining vector matching, semantic analysis matching and five-dimensional rule matching, the vector matching speed is high, and a large number of candidates can be quickly screened out; semantic analysis matching can understand the semantic context of the text, and supplement the deviation of vector matching in the aspect of semantic understanding; the five-dimensional rule matching can be comprehensively and carefully evaluated according to different recruitment conditions and post requirements, and the accuracy and reliability of a final matching result are ensured.

The invention discloses a wind turbine generator performance dynamic evaluation method and system based on multi-source data fusion, and the method comprises the steps: synchronizing multi-modal heterogeneous data through a quantum encryption algorithm and an edge gateway; constructing a space-time semantic graph network through a deep semantic analysis technology, and generating a precise space-time feature matrix; based on an attention mechanism, generating a physical enhancement feature vector; constructing cross-working-condition health index mapping by applying a machine learning algorithm, and quantifying a cross-working-condition comparable health index; a hierarchical incremental learning architecture is adopted, and a multi-objective optimization algorithm is utilized to generate a dynamic maintenance priority sequence; and building a digital twin platform, performing closed-loop verification on the maintenance priority sequence, and generating a unit maintenance scheme. The problem that multi-source data fusion of the wind turbine generator is difficult is solved, the health index quantification accuracy under the complex working condition is improved, the dynamic maintenance strategy is optimized, and closed-loop optimization of the maintenance scheme is achieved.

The invention discloses a reservoir safety intelligent inspection method and system based on YOLO and VLM fusion, and the method comprises the following steps: S1, multi-source data collection and preprocessing: employing an unmanned plane and ground equipment to collect image / video data, and carrying out the noise reduction, enhancement and space-time alignment processing of the data; s2, improving YOLO target detection: optimizing a network structure and a training strategy; s3, link analysis after VLM: target / scene association judgment is realized by adopting the VLM; and S4, report generation. The invention provides a reservoir safety intelligent inspection method and system fusing YOLO and VLM. Cross-modal semantic understanding, zero sample reasoning and video global analysis capabilities of a visual language large model are utilized, the visual language large model is used as a post-processing tool of YOLO and is fused with the post-processing tool to work in parallel, full-process intelligentization of target detection-semantic analysis-report generation can be realized, the existing technical problems are effectively solved, and the visual language large model has a wide application prospect. And the comprehensiveness of the hydraulic engineering safety monitoring system is enhanced.

A system for real-time analysis of emotional feedback during motivational speeches, consisting of: a series of multimodal sensors, including at least one visual sensor configured to capture facial expressions of spectators, at least one directional microphone configured to capture the audio responses of the audience, and optionally one or more physiological sensors configured to capture biometric signals from spectators; an edge-based processing unit that is communicatively coupled to the arrangement of multimodal sensors, wherein the edge-based processing unit comprises the following: (a) a feature extraction module configured to extract visual features from captured facial images, acoustic features from voice responses, and physiological features from biometric signals; (b) an emotion inference machine configured to process the features using a deep learning-based emotion recognition model comprising a convolutional neural network (CNN) for classifying facial expressions, a recurrent neural network (RNN) for classifying voice emotions, and a multimodal late fusion layer configured to compute a composite emotion state vector representing the aggregated emotions of the audience; (c) a timestamp and speech alignment module configured to correlate the calculated composite emotion state vector with segmented portions of a live motivational speech based on real-time speech-to-text transcription and semantic analysis; and (d) a session-based storage unit configured to log time-indexed emotional state vectors and corresponding speech segments for post-event analysis; A speaker feedback interface comprising a portable display or a podium-mounted visualization panel, wherein the interface is configured to display visual indicators of emotional feedback in real time, the indicators being derived from the emotional state vector and including at least emotional trend graphs, threshold alerts, or engagement indices.

The invention discloses an archive digitization full-life-cycle encryption integrity verification method and system, relates to the field of digital archive safety management, and is used for solving the problem of integrity and safety guarantee in archive digitization management. Logic units are divided through a semantic analysis engine, non-uniform blocks are generated based on sensitivity dynamic partitioning, local hash values are calculated, and a multi-level verification tree is constructed to generate root hash and a hash path mapping table; acquiring visitor identities, terminal environments and timestamps in each stage of an archive life cycle, generating state snapshots in combination with a hash path, and constructing a life cycle state snapshot chain; reconstructing a target logic unit verification path according to the access request, analyzing the structural difference between a current path and a historical snapshot chain through a path reconstruction type difference verification algorithm, and identifying structural offset, node tampering or path loss; and response control is triggered for abnormal verification, including session key updating and permission freezing, so that the security integrity guarantee of the digital archive in the full life cycle is improved.

The invention discloses an optical cable intelligent label full life cycle management method and system, and the method comprises the steps: extracting structured data through an XML analysis engine based on a configuration file, carrying out the semantic analysis of an unstructured text, carrying out the regression quantification of text features through combining symbol quantiles, and constructing a knowledge graph; according to the knowledge graph, dynamically adjusting the character size, the two-dimensional code position and the error-tolerant rate by adopting a generative adversarial network technology according to the optical cable type and the pasting scene, and generating optical cable intelligent labels adaptive to different scenes; collecting a label image, and converting the label image into structured label identification data including an optical cable type, a connection relation and a transmission signal type by adopting a computer vision technology; the identification data and the knowledge graph are compared, consistency is checked, potential abnormity is analyzed, influences are evaluated, and graded early warning is generated; a micro-service architecture system is constructed, data analysis, AI generation, an intelligent recognition engine and knowledge graph service are integrated, and full-life-cycle management is achieved. The intelligent level of full-life-cycle management of the optical cable label is improved.

The invention relates to an automatic video editing method based on semantic analysis, and belongs to the technical field of video editing. The method comprises the following steps: extracting anchor point information of text content in a streaming media file based on a knowledge graph, establishing a support relationship, and determining a main anchor point, a sub anchor point and a argument anchor point by combining the support relationship and the anchor point information; constructing a dynamic trigger mechanism, setting a verification direction of the dynamic trigger mechanism, determining a semantic extension direction of the anchor point information according to the verification direction, and verifying whether a precondition chain supporting the anchor point information exists or not; and setting a credibility scoring strategy, determining the credibility of the anchor point information according to the logic complexity, the language confidence and the text consistency, forming a sequence set of the anchor point information, and splicing to form a logically coherent video editing text result. According to the method, automatic and intelligent editing of the video is realized based on semantic analysis, and the editing efficiency and quality are remarkably improved.

Methods, systems, and computer programs are presented to perform semantic analysis of video data for event detection and validation. One method includes an operation for training a machine-learning model with text-image pairs to create a semantic model. The semantic model generates text embeddings based on text describing events and image embeddings from video frames. The system calculates similarity values between text and image embeddings to determine event occurrences when the similarity exceeds a predetermined threshold. The system enables precise event detection by analyzing object relationships and attributes within video frames, reducing false alarms and enhancing monitoring efficiency. Further, these techniques can be used to detect any event describable by human text or by some image examples.

The invention discloses a data analysis method and device, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining a target question proposed by a user; performing semantic analysis on the target problem, and determining a field corresponding to the target problem and semantic feature information of the target problem; determining a plurality of data processing tools related to the target problem based on the field and semantic feature information corresponding to the target problem; performing dynamic priority ranking on the plurality of data processing tools on the basis of correlation indexes of the plurality of data processing tools and the target problem and historical use conditions; and according to the dynamic priorities of the plurality of data processing tools, calling the corresponding data processing tools to analyze the target question, and generating and displaying an answer result corresponding to the target question. Compared with the prior art, the intelligent level and the execution efficiency of tool calling are enhanced, so that the overall service response capability and the user experience of the system are remarkably improved.

The invention discloses an eVTOL multi-camera cooperative video compression coding method based on multi-source perception and intelligent partitioning. The method comprises the following steps: constructing a scene three-dimensional perception model by fusing multi-source data of visible light, infrared and depth sensors; the method comprises the following steps: realizing dynamic video partitioning based on motion vectors and semantic analysis, and dividing a picture into a core region, a secondary region and a background region; establishing a parallax compensation motion prediction model by adopting a cross-camera reference frame sharing mechanism; and high-fidelity compression of the key area is realized through layered entropy coding and a dynamic quantization parameter distribution strategy. And the decoding end reversely executes multi-source data fusion and partition reconstruction according to the coding metadata. The method is suitable for eVTOL multi-camera video real-time transmission scenes such as polling, surveying and mapping.

The invention discloses a lecturer video automatic generation method based on AI speech synthesis and animation driving. The method comprises the following steps: performing structured analysis on a PPT or a text script through an improved interior point method and an incremental shortest path algorithm; performing semantic grouping by applying a full-dynamic parallel single-link clustering algorithm and generating an enhanced script with an expressive mark; a CosyVoice technology is combined with a low-rank approximation method to generate a high-quality voice data stream; establishing a mapping relation between contents and action expressions through semantic analysis, and generating a complete action expression instruction set; and driving the digital human model by using the msueTalk technology, and generating a final lecturer teaching video through a parallel rendering algorithm. According to the invention, the method achieves the efficient and automatic generation of the education video, remarkably improves the content production efficiency, reduces the production cost, and guarantees the specialty and expressive force of the teaching video.

The invention relates to the technical field of data asset exploration, in particular to an intelligent data asset exploration method and system based on a knowledge graph, and the method comprises the steps: obtaining enterprise data assets, carrying out the dynamic annotation, and generating an annotation data set; performing semantic analysis based on the annotation data set to obtain a semantic association graph; constructing a knowledge graph model, calculating node weights, association path lengths and path contribution degrees, and generating a simulation association distribution graph; segmenting the semantic association graph through the node density and the relationship strength to obtain an actual association distribution graph; and adjusting the knowledge graph model based on the actual and simulated association distribution histogram. According to the invention, the intelligent level and accuracy of data asset exploration can be improved.

The embodiment of the invention relates to the field of artificial intelligence, and provides a model context protocol injection attack protection method and device based on dynamic semantic analysis, computer equipment and a storage medium, and the method comprises the steps: obtaining request data corresponding to a call request initiated by a user through a model context protocol, carrying out the preprocessing of the request data, obtaining the preprocessed request data; performing semantic vectorization on request text and context historical information in the request data through a lightweight bidirectional encoder representation model to output an initial risk score; correcting the initial risk score according to context historical information carried in a model context protocol to obtain a corrected final risk score; and performing hierarchical defense decision according to the final risk score, and determining risk grading information corresponding to each piece of request data so as to execute a protection action corresponding to each piece of risk grading information. By adopting the method, the accuracy of identifying the protocol injection attack can be improved.

The invention discloses a progressive topic generation method based on semantic analysis and a knowledge graph. Performing preprocessing and semantic analysis on the question setting demand text, and extracting a necessary keyword set corresponding to the core knowledge points and an optional determiner set corresponding to the additional conditions; carrying out concept mapping in a college professional knowledge graph and associating with a course outline, constructing a hierarchical semantic constraint framework containing hard constraint and soft constraint, and carrying out consistency detection; adopting reverse index hard matching recall and knowledge graph soft extension recall to obtain candidate materials, and inputting the candidate materials into a field fine-tuning large language model to generate candidate questions; reordering is performed through multi-target learning ordering, teaching logic verification and quality evaluation are executed, and final questions are output; user feedback is received to form an incremental sample, and the generation model and the sorting model are updated, so that the accuracy, diversity and controllability of question generation are improved, and closed-loop optimization is supported.